add gptneox gguf example

convert-gptneox-h5-to-gguf.py

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+# Quick and dirty HF gptneox--> gguf conversion
+
+import gguf
+import sys
+import struct
+import json
+import numpy as np
+from typing import Any, List
+from pathlib import Path
+from transformers import AutoModelForCausalLM
+
+
+if len(sys.argv) < 3:
+    print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
+    print("  ftype == 0 -> float32")
+    print("  ftype == 1 -> float16")
+    sys.exit(1)
+
+
+# output in the same directory as the model
+dir_model = sys.argv[1]
+fname_out = sys.argv[1] + "/ggml-model.bin"
+
+
+# possible tensor data types
+#   ftype == 0 -> float32
+#   ftype == 1 -> float16
+#
+# map from ftype to string
+ftype_str = ["f32", "f16"]
+
+ftype = 1
+if len(sys.argv) > 2:
+    ftype = int(sys.argv[2])
+    if ftype < 0 or ftype > 1:
+        print("Invalid ftype: " + str(ftype))
+        sys.exit(1)
+    fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
+
+
+model = AutoModelForCausalLM.from_pretrained(dir_model, low_cpu_mem_usage=True, trust_remote_code=True)
+list_vars = model.state_dict()
+
+# count tensors to be converted
+tensor_count = 0
+for name in list_vars.keys():
+    # we don't need these
+    if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
+        continue
+    tensor_count += 1
+
+gguf_writer = gguf.GGUFWriter.open(fname_out)
+
+with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+    hparams = json.load(f)
+
+# This mmust be changed when adding/deleting kv
+kv_count = 14
+
+print("tensors " + str(tensor_count) + " kv " + str(kv_count))
+
+print("write gguf header")
+
+gguf_writer.write_header(tensor_count, kv_count)
+
+print("write gguf hparams")
+
+llm_arch = "gptneox"
+
+gguf_writer.write_name("pythia-70b-deduped")
+gguf_writer.write_description("gguf test model")
+gguf_writer.write_architecture(llm_arch)
+gguf_writer.write_context_length(llm_arch, hparams["max_position_embeddings"])
+gguf_writer.write_embedding_length(llm_arch, hparams["hidden_size"])
+gguf_writer.write_layer_count(llm_arch, hparams["num_hidden_layers"])
+gguf_writer.write_feed_forward_length(llm_arch, hparams["intermediate_size"])
+gguf_writer.write_rope_dimension_count(llm_arch, int( hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])) )
+gguf_writer.write_head_count(llm_arch, hparams["num_attention_heads"])
+gguf_writer.write_parallel_residual(llm_arch, hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
+gguf_writer.write_layer_norm_eps(llm_arch, hparams["layer_norm_eps"])
+
+# TOKENIZATION
+
+print("write gguf tokenizer")
+
+tokens: List[str] = []
+merges: List[str] = []
+
+if Path(dir_model + "/tokenizer.json").is_file():
+    # vocab type gpt2
+    print("Adding gpt2 tokenizer vocab")
+
+    with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
+        tokenizer = json.load(f)
+
+    for key in tokenizer["model"]["vocab"]:
+        tokens.append(key)
+
+    merges = tokenizer["model"]["merges"]
+
+gguf_writer.write_tokenizer_model("gpt2")
+gguf_writer.write_token_list(tokens)
+gguf_writer.write_token_merges(merges)
+
+# TENSORS
+
+# tensor info
+print("write gguf tensor info")
+
+for name in list_vars.keys():
+    data = list_vars[name].squeeze().numpy()
+
+    # we don't need these
+    if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
+        continue
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if ftype != 0:
+        if name.endswith(".weight") and n_dims == 2:
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            data = data.astype(np.float32)
+            ftype_cur = 0
+
+    gguf_writer.write_tensor_info(name, data)
+
+
+# tensor data
+print("write gguf tensor data")
+
+for name in list_vars.keys():
+    data = list_vars[name].squeeze().numpy()
+    print("Process tensor: " + name + " with shape: ", data.shape)
+
+    # we don't need these
+    if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
+        print("  Skip tensor: " + name)
+        continue
+
+    n_dims = len(data.shape)
+
+    # ftype == 0 -> float32, ftype == 1 -> float16
+    ftype_cur = 0
+    if ftype != 0:
+        if name.endswith(".weight") and n_dims == 2:
+            print("  Converting to float16")
+            data = data.astype(np.float16)
+            ftype_cur = 1
+        else:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+    else:
+        if data.dtype != np.float32:
+            print("  Converting to float32")
+            data = data.astype(np.float32)
+            ftype_cur = 0
+
+    gguf_writer.write_tensor(data)
+
+gguf_writer.close()
+
+
+print("Done. Output file: " + fname_out)
+print("")